
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns


df=pd.read_csv("C:\\Users\\91741\\Downloads\\train (2).csv")
df.shape

(1460, 81)


pd.set_option('display.max_columns',None)
pd.set_option('display.max_rows',None)
df.head()


df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1460 entries, 0 to 1459
Data columns (total 81 columns):
 #   Column         Non-Null Count  Dtype  
---  ------         --------------  -----  
 0   Id             1460 non-null   int64  
 1   MSSubClass     1460 non-null   int64  
 2   MSZoning       1460 non-null   object 
 3   LotFrontage    1201 non-null   float64
 4   LotArea        1460 non-null   int64  
 5   Street         1460 non-null   object 
 6   Alley          91 non-null     object 
 7   LotShape       1460 non-null   object 
 8   LandContour    1460 non-null   object 
 9   Utilities      1460 non-null   object 
 10  LotConfig      1460 non-null   object 
 11  LandSlope      1460 non-null   object 
 12  Neighborhood   1460 non-null   object 
 13  Condition1     1460 non-null   object 
 14  Condition2     1460 non-null   object 
 15  BldgType       1460 non-null   object 
 16  HouseStyle     1460 non-null   object 
 17  OverallQual    1460 non-null   int64  
 18  OverallCond    1460 non-null   int64  
 19  YearBuilt      1460 non-null   int64  
 20  YearRemodAdd   1460 non-null   int64  
 21  RoofStyle      1460 non-null   object 
 22  RoofMatl       1460 non-null   object 
 23  Exterior1st    1460 non-null   object 
 24  Exterior2nd    1460 non-null   object 
 25  MasVnrType     1452 non-null   object 
 26  MasVnrArea     1452 non-null   float64
 27  ExterQual      1460 non-null   object 
 28  ExterCond      1460 non-null   object 
 29  Foundation     1460 non-null   object 
 30  BsmtQual       1423 non-null   object 
 31  BsmtCond       1423 non-null   object 
 32  BsmtExposure   1422 non-null   object 
 33  BsmtFinType1   1423 non-null   object 
 34  BsmtFinSF1     1460 non-null   int64  
 35  BsmtFinType2   1422 non-null   object 
 36  BsmtFinSF2     1460 non-null   int64  
 37  BsmtUnfSF      1460 non-null   int64  
 38  TotalBsmtSF    1460 non-null   int64  
 39  Heating        1460 non-null   object 
 40  HeatingQC      1460 non-null   object 
 41  CentralAir     1460 non-null   object 
 42  Electrical     1459 non-null   object 
 43  1stFlrSF       1460 non-null   int64  
 44  2ndFlrSF       1460 non-null   int64  
 45  LowQualFinSF   1460 non-null   int64  
 46  GrLivArea      1460 non-null   int64  
 47  BsmtFullBath   1460 non-null   int64  
 48  BsmtHalfBath   1460 non-null   int64  
 49  FullBath       1460 non-null   int64  
 50  HalfBath       1460 non-null   int64  
 51  BedroomAbvGr   1460 non-null   int64  
 52  KitchenAbvGr   1460 non-null   int64  
 53  KitchenQual    1460 non-null   object 
 54  TotRmsAbvGrd   1460 non-null   int64  
 55  Functional     1460 non-null   object 
 56  Fireplaces     1460 non-null   int64  
 57  FireplaceQu    770 non-null    object 
 58  GarageType     1379 non-null   object 
 59  GarageYrBlt    1379 non-null   float64
 60  GarageFinish   1379 non-null   object 
 61  GarageCars     1460 non-null   int64  
 62  GarageArea     1460 non-null   int64  
 63  GarageQual     1379 non-null   object 
 64  GarageCond     1379 non-null   object 
 65  PavedDrive     1460 non-null   object 
 66  WoodDeckSF     1460 non-null   int64  
 67  OpenPorchSF    1460 non-null   int64  
 68  EnclosedPorch  1460 non-null   int64  
 69  3SsnPorch      1460 non-null   int64  
 70  ScreenPorch    1460 non-null   int64  
 71  PoolArea       1460 non-null   int64  
 72  PoolQC         7 non-null      object 
 73  Fence          281 non-null    object 
 74  MiscFeature    54 non-null     object 
 75  MiscVal        1460 non-null   int64  
 76  MoSold         1460 non-null   int64  
 77  YrSold         1460 non-null   int64  
 78  SaleType       1460 non-null   object 
 79  SaleCondition  1460 non-null   object 
 80  SalePrice      1460 non-null   int64  
dtypes: float64(3), int64(35), object(43)
memory usage: 924.0+ KB


df.describe()


features_nan=[feature for feature in df.columns if df[feature].isnull().sum()>0]
len(features_nan)

19


for feature in features_nan:
    print(feature,':',np.round(df[feature].isnull().mean()*100,2),'% missing values')

LotFrontage : 17.74 % missing values
Alley : 93.77 % missing values
MasVnrType : 0.55 % missing values
MasVnrArea : 0.55 % missing values
BsmtQual : 2.53 % missing values
BsmtCond : 2.53 % missing values
BsmtExposure : 2.6 % missing values
BsmtFinType1 : 2.53 % missing values
BsmtFinType2 : 2.6 % missing values
Electrical : 0.07 % missing values
FireplaceQu : 47.26 % missing values
GarageType : 5.55 % missing values
GarageYrBlt : 5.55 % missing values
GarageFinish : 5.55 % missing values
GarageQual : 5.55 % missing values
GarageCond : 5.55 % missing values
PoolQC : 99.52 % missing values
Fence : 80.75 % missing values
MiscFeature : 96.3 % missing values


sns.heatmap(df.isnull(),yticklabels=False,cmap='coolwarm')

<Axes: >


for feature in features_nan:
    data=df.copy()
    data[feature]=np.where(data[feature].isnull(),1,0)
    data.groupby(feature)['SalePrice'].median().plot.bar()
    plt.xlabel(feature)
    plt.ylabel('SalePrice')
    plt.title(feature)
    plt.show()


num_feat=[feature for feature in df.columns if df[feature].dtypes!='object']
len(num_feat)

38


num_nan=[feature for feature in num_feat if df[feature].isnull().sum()>0]
len(num_nan)

3


for feature in num_nan:
    print(feature,':',np.round(df[feature].isnull().mean()*100,2),'% missing values')

LotFrontage : 17.74 % missing values
MasVnrArea : 0.55 % missing values
GarageYrBlt : 5.55 % missing values


year_feat=[feature for feature in num_feat if 'Year' in feature or 'Yr'in feature]


len(year_feat)

4


data=df.copy()
data.groupby('YrSold')['SalePrice'].median().plot()
plt.xlabel('YrSold')
plt.ylabel('SalePrice')
plt.title(feature)

Text(0.5, 1.0, 'GarageYrBlt')


for feature in year_feat:
    data=df.copy()
    if feature!='YrSold':
        data[feature]=data['YrSold']-data[feature]
        plt.scatter(data[feature],data['SalePrice'])
        plt.xlabel(feature)
        plt.ylabel('SalePrice')
        plt.show()


## Above is True Picture of SalePrice with year so we use this for our year_feat modification


Disc_feat=[feature for feature in num_feat if len(df[feature].unique())<25 and feature not in year_feat+['Id']]
cont_feat=[feature for feature in num_feat if feature not in Disc_feat + year_feat+['Id']]


len(Disc_feat)

17


len(cont_feat)

16


for feature in Disc_feat:
    data=df.copy()
    data.groupby(feature)['SalePrice'].median().plot.bar()
    plt.xlabel(feature)
    plt.ylabel('SalePrice')
    plt.title(feature)
    plt.show()


for feature in cont_feat:
    data=df.copy()
    data[feature].hist(bins=20)
    plt.xlabel(feature)
    plt.ylabel('Count')
    plt.show()


for feature in cont_feat:
    sns.boxplot(df[feature])
    plt.title(feature)
    print(feature,':',df[feature].skew())
    plt.show()

LotFrontage : 2.163569142324884

LotArea : 12.207687851233496

MasVnrArea : 2.669084210182863

BsmtFinSF1 : 1.685503071910789

BsmtFinSF2 : 4.255261108933303

BsmtUnfSF : 0.9202684528039037

TotalBsmtSF : 1.5242545490627664

1stFlrSF : 1.3767566220336365

2ndFlrSF : 0.8130298163023265

GrLivArea : 1.3665603560164552


cont_feat
len(cont_feat)

16


featuress=['LotFrontage','LotArea', 'MasVnrArea', 'BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', '1stFlrSF',
           '2ndFlrSF','GrLivArea','WoodDeckSF', 'OpenPorchSF', 'EnclosedPorch', 'ScreenPorch','GarageArea']


s=[]
for feature in featuress:
    q1=df[feature].quantile(0.25)
    q3=df[feature].quantile(0.75)
    iqr=q3-q1
    upper_bridge=q3+1.5*iqr
    lower_bridge=q1-1.5*iqr
    l=upper_bridge,lower_bridge
    print(feature,':',df[feature].skew())
    s.append(l)
print(s)

LotFrontage : 2.163569142324884
LotArea : 12.207687851233496
MasVnrArea : 2.669084210182863
BsmtFinSF1 : 1.685503071910789
BsmtFinSF2 : 4.255261108933303
BsmtUnfSF : 0.9202684528039037
TotalBsmtSF : 1.5242545490627664
1stFlrSF : 1.3767566220336365
2ndFlrSF : 0.8130298163023265
GrLivArea : 1.3665603560164552
WoodDeckSF : 1.5413757571931312
OpenPorchSF : 2.3643417403694404
EnclosedPorch : 3.08987190371177
ScreenPorch : 4.122213743143115
GarageArea : 0.17998090674623907
[(111.5, 27.5), (17673.5, 1481.5), (415.0, -249.0), (1780.625, -1068.375), (0.0, 0.0), (1685.5, -654.5), (2052.0, 42.0), (2155.125, 118.125), (1820.0, -1092.0), (2747.625, 158.625), (420.0, -252.0), (170.0, -102.0), (0.0, 0.0), (0.0, 0.0), (938.25, -27.75)]


s=[(111.5, 27.5), (17673.5, 1481.5), (415.0, -249.0), (1780.625, -1068.375), (0.0, 0.0), (1685.5, -654.5), (2052.0, 42.0), (2155.125, 118.125), (1820.0, -1092.0), (2747.625, 158.625), (420.0, -252.0), (170.0, -102.0), (0.0, 0.0), (0.0, 0.0), (938.25, -27.75)]
def impute(date, featuress, s):
    for idx, feature in enumerate(featuress):
        upper_bridge, lower_bridge = s[idx]
        date[feature] = np.where(df[feature] > upper_bridge, upper_bridge,
                                np.where(df[feature] < lower_bridge, lower_bridge,
                                         df[feature]))


for feat in featuress:
    sns.boxplot(df[feat])
    plt.title(feat)
    plt.show()


df.head()


cat_var=[feature for feature in df.columns if df[feature].dtypes=='object']
len(cat_var)

43


cat_nan=[feature for feature in cat_var if df[feature].isnull().sum()>0]
len(cat_nan)

16


for feature in cat_nan:
    print(feature,':',np.round(df[feature].isnull().mean()*100,2),'% missing values')

Alley : 93.77 % missing values
MasVnrType : 0.55 % missing values
BsmtQual : 2.53 % missing values
BsmtCond : 2.53 % missing values
BsmtExposure : 2.6 % missing values
BsmtFinType1 : 2.53 % missing values
BsmtFinType2 : 2.6 % missing values
Electrical : 0.07 % missing values
FireplaceQu : 47.26 % missing values
GarageType : 5.55 % missing values
GarageFinish : 5.55 % missing values
GarageQual : 5.55 % missing values
GarageCond : 5.55 % missing values
PoolQC : 99.52 % missing values
Fence : 80.75 % missing values
MiscFeature : 96.3 % missing values


for feature in cat_var:
    data=df.copy()
    data.groupby(feature)['SalePrice'].median().plot.bar()
    plt.title(feature)
    plt.xlabel(feature)
    plt.ylabel('Median Saleprice')
    plt.show()


## From above we can see how different features are affecting saleprice


from sklearn.model_selection import train_test_split
train,test = train_test_split(df,test_size=0.2,random_state=42)


train.shape, test.shape

((1168, 81), (292, 81))


cat_nan_features=[feature for feature in train.columns if train[feature].dtypes=='object' and train[feature].isnull().sum()>0]


len(cat_nan_features)

16


for feature in cat_nan_features:
    print(feature,':',np.round(train[feature].isnull().mean()*100,2),'% missing values')

Alley : 93.66 % missing values
MasVnrType : 0.51 % missing values
BsmtQual : 2.4 % missing values
BsmtCond : 2.4 % missing values
BsmtExposure : 2.4 % missing values
BsmtFinType1 : 2.4 % missing values
BsmtFinType2 : 2.4 % missing values
Electrical : 0.09 % missing values
FireplaceQu : 46.83 % missing values
GarageType : 5.48 % missing values
GarageFinish : 5.48 % missing values
GarageQual : 5.48 % missing values
GarageCond : 5.48 % missing values
PoolQC : 99.49 % missing values
Fence : 80.05 % missing values
MiscFeature : 96.06 % missing values


def replace_nan(train,cat_nan_features):
    dff=train.copy()
    dff[cat_nan_features]=dff[cat_nan_features].fillna('Missing')
    return dff


train=replace_nan(train,cat_nan_features)
train[cat_nan_features].isnull().sum()

Alley           0
MasVnrType      0
BsmtQual        0
BsmtCond        0
BsmtExposure    0
BsmtFinType1    0
BsmtFinType2    0
Electrical      0
FireplaceQu     0
GarageType      0
GarageFinish    0
GarageQual      0
GarageCond      0
PoolQC          0
Fence           0
MiscFeature     0
dtype: int64


train.shape

(1168, 81)


num_nan_features = [feature for feature in train.columns if train[feature].dtypes!='O' and train[feature].isnull().sum()>0]
len(num_nan_features)

3


for feature in num_nan_features:
    print(feature,':',np.round(df[feature].isnull().mean()*100,2),'% missing values')

LotFrontage : 17.74 % missing values
MasVnrArea : 0.55 % missing values
GarageYrBlt : 5.55 % missing values


for feature in num_nan_features:
    median_value=train[feature].median()
    train[feature+"_nan"] = np.where(train[feature].isnull(),1,0)
    train[feature].fillna(median_value,inplace=True)


train[num_nan_features].isnull().sum()

LotFrontage    0
MasVnrArea     0
GarageYrBlt    0
dtype: int64


train.shape

(1168, 84)


year_feat

['YearBuilt', 'YearRemodAdd', 'GarageYrBlt', 'YrSold']


features=['YearBuilt', 'YearRemodAdd', 'GarageYrBlt']
for feature in year_feat:
    train[feature]=train['YrSold']-train[feature]


train[['YearBuilt', 'YearRemodAdd', 'GarageYrBlt']].head()


train.head()


train.columns

Index(['Id', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street',
       'Alley', 'LotShape', 'LandContour', 'Utilities', 'LotConfig',
       'LandSlope', 'Neighborhood', 'Condition1', 'Condition2', 'BldgType',
       'HouseStyle', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd',
       'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd', 'MasVnrType',
       'MasVnrArea', 'ExterQual', 'ExterCond', 'Foundation', 'BsmtQual',
       'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinSF1',
       'BsmtFinType2', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', 'Heating',
       'HeatingQC', 'CentralAir', 'Electrical', '1stFlrSF', '2ndFlrSF',
       'LowQualFinSF', 'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath', 'FullBath',
       'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr', 'KitchenQual',
       'TotRmsAbvGrd', 'Functional', 'Fireplaces', 'FireplaceQu', 'GarageType',
       'GarageYrBlt', 'GarageFinish', 'GarageCars', 'GarageArea', 'GarageQual',
       'GarageCond', 'PavedDrive', 'WoodDeckSF', 'OpenPorchSF',
       'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'PoolQC',
       'Fence', 'MiscFeature', 'MiscVal', 'MoSold', 'YrSold', 'SaleType',
       'SaleCondition', 'SalePrice', 'LotFrontage_nan', 'MasVnrArea_nan',
       'GarageYrBlt_nan'],
      dtype='object')


for feature in train.columns:
    plt.hist(train[feature],bins=20)
    plt.title(feature)
    plt.show()


Disc_feat=[feature for feature in num_feat if len(train[feature].unique())<25 and feature not in year_feat+['Id']]
cont_feat=[feature for feature in num_feat if feature not in Disc_feat + year_feat+['Id']]


x=[feature for feature in train.columns if train[feature].dtypes!='object'and feature in cont_feat]

x

['LotFrontage',
 'LotArea',
 'MasVnrArea',
 'BsmtFinSF1',
 'BsmtUnfSF',
 'TotalBsmtSF',
 '1stFlrSF',
 '2ndFlrSF',
 'GrLivArea',
 'GarageArea',
 'WoodDeckSF',
 'OpenPorchSF',
 'SalePrice']


for feature in x:
    print(feature,':',train[feature].skew())

LotFrontage : 0.07906515289033489
LotArea : 0.19342857588029116
MasVnrArea : 1.2748890788415927
BsmtFinSF1 : 0.7230258028627954
BsmtUnfSF : 0.7904304940072501
TotalBsmtSF : 0.23478699338365772
1stFlrSF : 0.6113893091750534
2ndFlrSF : 0.7878996002666335
GrLivArea : 0.5796735095315892
GarageArea : -0.11152945885964144
WoodDeckSF : 1.0903417142244038
OpenPorchSF : 1.0545071966412067
SalePrice : 1.743128561420854


skew_feature=['MasVnrArea','BsmtFinSF1','BsmtUnfSF','1stFlrSF','2ndFlrSF','GrLivArea','WoodDeckSF','OpenPorchSF','SalePrice']


import scipy.stats as stat
for feature in skew_feature:
    train[feature],parameters=stat.boxcox(train[feature]+1)
    print(feature,':',train[feature].skew())

MasVnrArea : 0.37499775630254123
BsmtFinSF1 : -0.42497082737217173
BsmtUnfSF : -0.28761928691335925
1stFlrSF : -0.004253922621941451
2ndFlrSF : 0.25972021556529684
GrLivArea : -0.007010546332412592
WoodDeckSF : 0.15517682491755982
OpenPorchSF : -0.06588271709629863
SalePrice : -0.008051284584600405


train.head()


for feature in skew_feature:
    sns.histplot(train[feature])
    print(feature,':',train[feature].skew())
    plt.show()

MasVnrArea : 0.37499775630254123

BsmtFinSF1 : -0.42497082737217173

BsmtUnfSF : -0.28761928691335925

1stFlrSF : -0.004253922621941451

2ndFlrSF : 0.25972021556529684

GrLivArea : -0.007010546332412592

WoodDeckSF : 0.15517682491755982

OpenPorchSF : -0.06588271709629863

SalePrice : -0.008051284584600405


for feature in cat_var:
    temp=train.groupby(feature)['SalePrice'].count()/len(train)
    temp_df=temp[temp>0.01].index
    train[feature] = np.where( train[feature].isin(temp_df), train[feature], 'Rare_var' )


all_labels=[]
for feature in cat_var:
    labels_ordered=train.groupby(feature)['SalePrice'].mean().sort_values().index
    labels_ordered={k:i for i,k in enumerate(labels_ordered,0)}
    all_labels.append(labels_ordered)
    train[feature]=train[feature].map(labels_ordered)
print(all_labels)

[{'Rare_var': 0, 'RM': 1, 'RH': 2, 'RL': 3, 'FV': 4}, {'Rare_var': 0, 'Pave': 1}, {'Grvl': 0, 'Pave': 1, 'Missing': 2}, {'Reg': 0, 'IR1': 1, 'Rare_var': 2, 'IR2': 3}, {'Bnk': 0, 'Lvl': 1, 'Low': 2, 'HLS': 3}, {'Rare_var': 0, 'AllPub': 1}, {'Corner': 0, 'Inside': 1, 'FR2': 2, 'Rare_var': 3, 'CulDSac': 4}, {'Gtl': 0, 'Mod': 1, 'Rare_var': 2}, {'BrDale': 0, 'IDOTRR': 1, 'BrkSide': 2, 'Edwards': 3, 'OldTown': 4, 'Sawyer': 5, 'SWISU': 6, 'NAmes': 7, 'Rare_var': 8, 'Mitchel': 9, 'SawyerW': 10, 'NWAmes': 11, 'Blmngtn': 12, 'Gilbert': 13, 'CollgCr': 14, 'Crawfor': 15, 'ClearCr': 16, 'Somerst': 17, 'Timber': 18, 'StoneBr': 19, 'NridgHt': 20, 'NoRidge': 21}, {'Artery': 0, 'Feedr': 1, 'Norm': 2, 'Rare_var': 3, 'RRAn': 4, 'PosN': 5}, {'Rare_var': 0, 'Norm': 1}, {'2fmCon': 0, 'Twnhs': 1, 'Duplex': 2, '1Fam': 3, 'TwnhsE': 4}, {'1.5Unf': 0, '1.5Fin': 1, 'SFoyer': 2, '1Story': 3, 'SLvl': 4, 'Rare_var': 5, '2Story': 6}, {'Gable': 0, 'Rare_var': 1, 'Hip': 2}, {'CompShg': 0, 'Rare_var': 1}, {'AsbShng': 0, 'Rare_var': 1, 'WdShing': 2, 'Wd Sdng': 3, 'MetalSd': 4, 'HdBoard': 5, 'Stucco': 6, 'Plywood': 7, 'BrkFace': 8, 'VinylSd': 9, 'CemntBd': 10}, {'AsbShng': 0, 'Wd Sdng': 1, 'MetalSd': 2, 'Wd Shng': 3, 'Stucco': 4, 'Rare_var': 5, 'HdBoard': 6, 'Plywood': 7, 'VinylSd': 8, 'BrkFace': 9, 'CmentBd': 10}, {'BrkCmn': 0, 'None': 1, 'BrkFace': 2, 'Rare_var': 3, 'Stone': 4}, {'Rare_var': 0, 'TA': 1, 'Gd': 2, 'Ex': 3}, {'Fa': 0, 'Rare_var': 1, 'Gd': 2, 'TA': 3}, {'Slab': 0, 'BrkTil': 1, 'CBlock': 2, 'Rare_var': 3, 'PConc': 4}, {'Missing': 0, 'Fa': 1, 'TA': 2, 'Gd': 3, 'Ex': 4}, {'Rare_var': 0, 'Missing': 1, 'Fa': 2, 'TA': 3, 'Gd': 4}, {'Missing': 0, 'No': 1, 'Mn': 2, 'Av': 3, 'Gd': 4}, {'Missing': 0, 'Rec': 1, 'LwQ': 2, 'BLQ': 3, 'ALQ': 4, 'Unf': 5, 'GLQ': 6}, {'Missing': 0, 'BLQ': 1, 'Rec': 2, 'LwQ': 3, 'Unf': 4, 'Rare_var': 5, 'ALQ': 6}, {'Rare_var': 0, 'GasW': 1, 'GasA': 2}, {'Rare_var': 0, 'Fa': 1, 'TA': 2, 'Gd': 3, 'Ex': 4}, {'N': 0, 'Y': 1}, {'FuseF': 0, 'Rare_var': 1, 'FuseA': 2, 'SBrkr': 3}, {'Fa': 0, 'TA': 1, 'Gd': 2, 'Ex': 3}, {'Rare_var': 0, 'Mod': 1, 'Min2': 2, 'Min1': 3, 'Typ': 4}, {'Po': 0, 'Missing': 1, 'Fa': 2, 'TA': 3, 'Gd': 4, 'Ex': 5}, {'Missing': 0, 'Rare_var': 1, 'Detchd': 2, 'Basment': 3, 'Attchd': 4, 'BuiltIn': 5}, {'Missing': 0, 'Unf': 1, 'RFn': 2, 'Fin': 3}, {'Missing': 0, 'Fa': 1, 'Rare_var': 2, 'TA': 3, 'Gd': 4}, {'Missing': 0, 'Fa': 1, 'Rare_var': 2, 'TA': 3}, {'N': 0, 'P': 1, 'Y': 2}, {'Missing': 0, 'Rare_var': 1}, {'GdWo': 0, 'Rare_var': 1, 'MnPrv': 2, 'Missing': 3, 'GdPrv': 4}, {'Rare_var': 0, 'Shed': 1, 'Missing': 2}, {'COD': 0, 'Rare_var': 1, 'WD': 2, 'New': 3}, {'Abnorml': 0, 'Family': 1, 'Rare_var': 2, 'Normal': 3, 'Partial': 4}]


train.head()


from sklearn.preprocessing import MinMaxScaler
scaled_feat = [feature for feature in train.columns if feature not in ['Id','SalePrice'] ]
s=MinMaxScaler()
s.fit(train[scaled_feat])
traine_data=pd.concat([train[['Id','SalePrice']].reset_index(drop=True),pd.DataFrame(s.transform(train[scaled_feat]),columns=scaled_feat)],axis=1)


traine_data.head()


x_train = traine_data.drop(['Id','SalePrice'], axis=1)
y_train = traine_data['SalePrice']
x_train.shape , y_train.shape

((1168, 82), (1168,))


traine_data.to_csv('traine_.csv', index=False)


cat_nan_feat=[feature for feature in test.columns if test[feature].dtypes=='object'and test[feature].isnull().sum()>0]


len(cat_nan_feat)

15


def replace_nan(test,cat_nan_feat):
    for feature in cat_nan_feat:
        test[feature].fillna('Missing',inplace=True)
replace_nan(test,cat_nan_feat)


test[cat_nan_feat].isnull().sum()

Alley           0
MasVnrType      0
BsmtQual        0
BsmtCond        0
BsmtExposure    0
BsmtFinType1    0
BsmtFinType2    0
FireplaceQu     0
GarageType      0
GarageFinish    0
GarageQual      0
GarageCond      0
PoolQC          0
Fence           0
MiscFeature     0
dtype: int64


for feature in cat_var:
    temp=test.groupby(feature)['SalePrice'].count()/len(test)
    temp_df=temp[temp>0.01]
    test[feature]=np.where(test[feature].isin(temp_df),test[feature],'rare_var')


for feature in cat_var:
    labels_ordered=test.groupby(feature)['SalePrice'].mean().sort_values().index
    labels_ordered={k:i for i,k in enumerate(labels_ordered,0)}
    test[feature]=test[feature].map(labels_ordered)


test.head()


num_nan_features = [feature for feature in test.columns if test[feature].dtypes!='O' and test[feature].isnull().sum()>0]

for feature in num_nan_features:
    median=test[feature].median()
    
    test[feature+"_nan"] = np.where(test[feature].isnull(),1,0)
    test[feature].fillna(median,inplace=True)
    
test[num_nan_features].isnull().sum()

LotFrontage    0
MasVnrArea     0
GarageYrBlt    0
dtype: int64


year_feat

['YearBuilt', 'YearRemodAdd', 'GarageYrBlt', 'YrSold']


features=['YearBuilt', 'YearRemodAdd', 'GarageYrBlt']
for feature in features:
    test[feature]=test['YrSold']-test[feature]


test.columns

Index(['Id', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street',
       'Alley', 'LotShape', 'LandContour', 'Utilities', 'LotConfig',
       'LandSlope', 'Neighborhood', 'Condition1', 'Condition2', 'BldgType',
       'HouseStyle', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd',
       'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd', 'MasVnrType',
       'MasVnrArea', 'ExterQual', 'ExterCond', 'Foundation', 'BsmtQual',
       'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinSF1',
       'BsmtFinType2', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', 'Heating',
       'HeatingQC', 'CentralAir', 'Electrical', '1stFlrSF', '2ndFlrSF',
       'LowQualFinSF', 'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath', 'FullBath',
       'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr', 'KitchenQual',
       'TotRmsAbvGrd', 'Functional', 'Fireplaces', 'FireplaceQu', 'GarageType',
       'GarageYrBlt', 'GarageFinish', 'GarageCars', 'GarageArea', 'GarageQual',
       'GarageCond', 'PavedDrive', 'WoodDeckSF', 'OpenPorchSF',
       'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'PoolQC',
       'Fence', 'MiscFeature', 'MiscVal', 'MoSold', 'YrSold', 'SaleType',
       'SaleCondition', 'SalePrice', 'LotFrontage_nan', 'MasVnrArea_nan',
       'GarageYrBlt_nan'],
      dtype='object')


for feature in test.columns:
    plt.hist(test[feature],bins=20)
    plt.title(feature)
    plt.show()


Disc_feat=[feature for feature in num_feat if len(test[feature].unique())<25 and feature not in year_feat+['Id']]
cont_feat=[feature for feature in num_feat if feature not in Disc_feat + year_feat+['Id']]


x=[feature for feature in test.columns if test[feature].dtypes!='object'and feature in cont_feat]

x

['LotFrontage',
 'LotArea',
 'MasVnrArea',
 'BsmtFinSF1',
 'BsmtUnfSF',
 'TotalBsmtSF',
 '1stFlrSF',
 '2ndFlrSF',
 'GrLivArea',
 'GarageArea',
 'WoodDeckSF',
 'OpenPorchSF',
 'SalePrice']


for feature in x:
    print(feature,':',test[feature].skew())

LotFrontage : 0.13525007860287586
LotArea : 0.278935292144115
MasVnrArea : 1.407458483609809
BsmtFinSF1 : 0.8102608510060361
BsmtUnfSF : 0.8187601521572084
TotalBsmtSF : 0.2527861304487117
1stFlrSF : 0.8841106021878509
2ndFlrSF : 0.850190191444724
GrLivArea : 0.6744274686688129
GarageArea : 0.0945189419839681
WoodDeckSF : 0.9945802382684821
OpenPorchSF : 1.509618543232546
SalePrice : 2.2643948196066


skew_features=['MasVnrArea','BsmtFinSF1','BsmtUnfSF','1stFlrSF','2ndFlrSF','GrLivArea','WoodDeckSF','OpenPorchSF','SalePrice']


import scipy.stats as stat
for feature in skew_features:
    test[feature],parameters=stat.boxcox(test[feature]+1)
    print(feature,':',test[feature].skew())

MasVnrArea : 0.6309412532135422
BsmtFinSF1 : -0.3982084098229691
BsmtUnfSF : -0.32507601106345896
1stFlrSF : 0.008558223691437283
2ndFlrSF : 0.3679919122602979
GrLivArea : -0.006237786167460394
WoodDeckSF : 0.031061833934702614
OpenPorchSF : 0.08411681166435096
SalePrice : -0.012004687957725873


for feature in skew_features:
    sns.histplot(test[feature])
    print(feature,':',test[feature].skew())
    plt.show()

MasVnrArea : 0.6309412532135422

BsmtFinSF1 : -0.3982084098229691

BsmtUnfSF : -0.32507601106345896

1stFlrSF : 0.008558223691437283

2ndFlrSF : 0.3679919122602979

GrLivArea : -0.006237786167460394

WoodDeckSF : 0.031061833934702614

OpenPorchSF : 0.08411681166435096

SalePrice : -0.012004687957725873


test.head()


from sklearn.preprocessing import MinMaxScaler
scaled_feat = [feature for feature in test.columns if feature not in ['Id','SalePrice'] ]
s=MinMaxScaler()
s.fit(test[scaled_feat])
test_data=pd.concat([test[['Id','SalePrice']].reset_index(drop=True),pd.DataFrame(s.transform(test[scaled_feat]),columns=scaled_feat)],axis=1)


test_data.head()


x_test = test_data.drop(['Id','SalePrice'], axis=1)
y_test = test_data['SalePrice']
x_test.shape , y_test.shape

((292, 82), (292,))


from sklearn.linear_model import Lasso
from sklearn.feature_selection import SelectFromModel
# SelectFromModel : selects features from model whose coefficient is non-zero


model=SelectFromModel(Lasso(alpha=0.005,random_state=0))
# Always remember the seed value(random_state value); you have to use same value for test dataset
## More the alpha less features required
model.fit(x_train,y_train)

SelectFromModel(estimator=Lasso(alpha=0.005, random_state=0))

SelectFromModel(estimator=Lasso(alpha=0.005, random_state=0))

Lasso(alpha=0.005, random_state=0)

Lasso(alpha=0.005, random_state=0)


model.get_support()

array([False, False, False,  True, False, False, False, False, False,
       False, False,  True, False, False, False, False,  True, False,
       False,  True, False, False, False, False, False,  True, False,
       False, False, False, False, False, False,  True, False, False,
       False,  True, False, False, False, False, False, False, False,
        True, False, False, False, False, False, False,  True, False,
       False, False,  True,  True, False,  True, False,  True, False,
       False, False,  True,  True, False, False, False, False, False,
       False, False, False, False, False, False, False, False, False,
       False])


selected_feat=x_train.columns[(model.get_support())]
selected_feat

Index(['LotArea', 'Neighborhood', 'OverallQual', 'YearRemodAdd', 'MasVnrArea',
       'BsmtFinSF1', 'TotalBsmtSF', 'GrLivArea', 'KitchenQual', 'FireplaceQu',
       'GarageType', 'GarageFinish', 'GarageArea', 'WoodDeckSF',
       'OpenPorchSF'],
      dtype='object')


print('Total features: {}'.format(x_train.shape[1]))
print('selected features: {}'.format(len(selected_feat)))
print("features with coeffecient zero: {}".format(np.sum(model.estimator_.coef_ == 0)))

Total features: 82
selected features: 15
features with coeffecient zero: 67


train_x = x_train[selected_feat]
train_y = traine_data['SalePrice']

test_x = x_test[selected_feat]
test_y = test_data['SalePrice']

train_x.shape , train_y.shape
test_x.shape,test_y.shape

((292, 15), (292,))


plt.figure(figsize=(10,10))
cor=train_x.corr()
sns.heatmap(cor,annot=True,cmap='coolwarm')

<Axes: >


from sklearn.model_selection import cross_val_score
from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score 
from sklearn.metrics import mean_squared_error
from sklearn.metrics import mean_absolute_error
l=LinearRegression()
l.fit(x_train,y_train)
y_pred=l.predict(x_test)


from sklearn.metrics import r2_score
r2_score(y_pred,test_y)

-1.8149227249625457


from sklearn.model_selection import KFold
folds=KFold(n_splits=5,shuffle=True,random_state=100)
scores=cross_val_score(l,train_x,train_y,scoring='r2',cv=5)
scores.mean()

0.872984794341404


from sklearn import metrics
linear_score = cross_val_score(l,test_x,test_y,cv=5)
print('Cross Val Score: ', linear_score.mean())
print('Mean Absolute Error:', mean_absolute_error(test_y, y_pred))
print('Mean Squared Error:',mean_squared_error(test_y, y_pred))

linear_RMSE = np.sqrt(mean_squared_error(test_y, y_pred))
print('Root Mean Squared Error:', linear_RMSE )

Cross Val Score:  0.8564504681790399
Mean Absolute Error: 69.25674559492849
Mean Squared Error: 7436.544527847517
Root Mean Squared Error: 86.2354018245843


from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import KFold
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import PolynomialFeatures
from sklearn.linear_model import Ridge


kf = KFold(shuffle=True, random_state=0, n_splits=3)

estimator = Pipeline([("polynomial_features", PolynomialFeatures()),
                      ("ridge_regression", Ridge())])

params = {
    'polynomial_features__degree': [1, 2, 3],
    'ridge_regression__alpha': [1e-15,1e-10,1e-8,1e-3,0.005,1e-2,0.05,0.1,0.5,1,5,10,20,30,35,40]
}
# 'alpha':[1e-15,1e-10,1e-8,1e-3,0.005,1e-2,0.05,0.1,0.5,1,5,10,20,30,35,40]

grid = GridSearchCV(estimator, params, cv=kf)

grid.fit(train_x,train_y)
print('best score: {}'.format(grid.best_score_))
print('best score: {}'.format(grid.best_params_))

C:\ProgramData\anaconda3\lib\site-packages\sklearn\linear_model\_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=2.03888e-18): result may not be accurate.
  return linalg.solve(A, Xy, assume_a="pos", overwrite_a=True).T
C:\ProgramData\anaconda3\lib\site-packages\sklearn\linear_model\_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=1.95988e-18): result may not be accurate.
  return linalg.solve(A, Xy, assume_a="pos", overwrite_a=True).T
C:\ProgramData\anaconda3\lib\site-packages\sklearn\linear_model\_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=1.97335e-18): result may not be accurate.
  return linalg.solve(A, Xy, assume_a="pos", overwrite_a=True).T
C:\ProgramData\anaconda3\lib\site-packages\sklearn\linear_model\_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=2.30314e-19): result may not be accurate.
  return linalg.solve(A, Xy, assume_a="pos", overwrite_a=True).T
C:\ProgramData\anaconda3\lib\site-packages\sklearn\linear_model\_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=2.21057e-19): result may not be accurate.
  return linalg.solve(A, Xy, assume_a="pos", overwrite_a=True).T
C:\ProgramData\anaconda3\lib\site-packages\sklearn\linear_model\_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=2.25051e-19): result may not be accurate.
  return linalg.solve(A, Xy, assume_a="pos", overwrite_a=True).T
C:\ProgramData\anaconda3\lib\site-packages\sklearn\linear_model\_ridge.py:255: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.
  warnings.warn(
C:\ProgramData\anaconda3\lib\site-packages\sklearn\linear_model\_ridge.py:255: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.
  warnings.warn(
C:\ProgramData\anaconda3\lib\site-packages\sklearn\linear_model\_ridge.py:253: LinAlgWarning: Ill-conditioned matrix (rcond=1.03569e-18): result may not be accurate.
  dual_coef = linalg.solve(K, y, assume_a="pos", overwrite_a=False)

best score: 0.8710007754103465
best score: {'polynomial_features__degree': 1, 'ridge_regression__alpha': 0.1}


ridge = Ridge(alpha=0.1)

ridge.fit(train_x,train_y)
y_pred = ridge.predict(test_x)

ridge_score = cross_val_score(ridge,test_x,test_y,cv=10)
print('Cross Val Score: ', ridge_score.mean())

ridge_RMSE = np.sqrt(mean_squared_error(test_y, y_pred))
print('Root Mean Squared Error:', ridge_RMSE)

Cross Val Score:  0.8606347335018991
Root Mean Squared Error: 0.27740277050635215


from sklearn.model_selection import GridSearchCV

lasso=Lasso()
parameters={'alpha':[1e-15,1e-10,1e-8,1e-3,0.005,1e-2,0.05,0.1,0.5,1,5,10,20,30,35,40]}
lasso_regressor=GridSearchCV(lasso,parameters,cv=kf)

lasso_regressor.fit(x_train,y_train)
print(lasso_regressor.best_params_)
print(lasso_regressor.best_score_)

C:\ProgramData\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.409e-01, tolerance: 1.564e-03
  model = cd_fast.enet_coordinate_descent(
C:\ProgramData\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.778e-02, tolerance: 1.609e-03
  model = cd_fast.enet_coordinate_descent(
C:\ProgramData\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.655e-01, tolerance: 1.575e-03
  model = cd_fast.enet_coordinate_descent(
C:\ProgramData\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.907e-01, tolerance: 1.564e-03
  model = cd_fast.enet_coordinate_descent(
C:\ProgramData\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.536e-02, tolerance: 1.564e-03
  model = cd_fast.enet_coordinate_descent(

{'alpha': 1e-15}
0.8913916436750625

C:\ProgramData\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.678e-01, tolerance: 2.374e-03
  model = cd_fast.enet_coordinate_descent(


lasso = Lasso(alpha=0.001)

lasso.fit(train_x,train_y)
y_pred = lasso.predict(test_x)

lasso_score = cross_val_score(lasso,test_x,test_y,cv=10)
print('Cross Val Score: ', lasso_score.mean())

lasso_RMSE = np.sqrt(mean_squared_error(test_y, y_pred))
print('Root Mean Squared Error:', lasso_RMSE)

Cross Val Score:  0.8607689533495189
Root Mean Squared Error: 0.2765120843288113


print('Linear Regression score: {} , RMSE: {}'.format(linear_score.mean(),linear_RMSE))
print('Ridge Regression score: {} , RMSE: {}'.format(ridge_score.mean(),ridge_RMSE))
print('Lasso Regression score: {} , RMSE: {}'.format(lasso_score.mean() ,lasso_RMSE))

Linear Regression score: 0.8564504681790399 , RMSE: 86.2354018245843
Ridge Regression score: 0.8606347335018991 , RMSE: 0.27740277050635215
Lasso Regression score: 0.8607689533495189 , RMSE: 0.2765120843288113


from sklearn.svm import SVR

svm_regressor = SVR()
parameters = {'kernel':['poly','rbf'] , 'degree':[2,3,4,5] }
svm_reg = GridSearchCV(svm_regressor,parameters,cv=kf)

svm_reg.fit(train_x,train_y)
print('Best Parameters: {}'.format(svm_reg.best_params_))
print('Best SCore: {}'.format(svm_reg.best_score_))

Best Parameters: {'degree': 2, 'kernel': 'rbf'}
Best SCore: 0.8001388309664638


svm_regressor = SVR(kernel='rbf', degree=2)
svm_regressor.fit(train_x,train_y)
y_pred = svm_regressor.predict(test_x)

SVM_score = cross_val_score(svm_regressor,test_x,test_y,cv=10)
print('Cross Val Score: ', SVM_score.mean())

SVM_RMSE = np.sqrt(mean_squared_error(test_y, y_pred))
print('Root Mean Squared Error:', SVM_RMSE)

Cross Val Score:  0.7691136815622601
Root Mean Squared Error: 0.30500190639881103


from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import RandomizedSearchCV

rf = RandomForestRegressor()


 #Randomized Search CV

# Number of trees in random forest
n_estimators = [int(x) for x in np.linspace(start = 100, stop = 1200, num = 12)] 

# Number of features to consider at every split
max_features = ['auto', 'sqrt']

# Maximum number of levels in tree
max_depth = [int(x) for x in np.linspace(5, 30, num = 6)]
# max_depth.append(None)

# Minimum number of samples required to split a node
min_samples_split = [2, 5, 10, 15, 100]

# Minimum number of samples required at each leaf node
min_samples_leaf = [1, 2, 5, 10]
# Method of selecting samples for training each tree
# bootstrap = [True, False]

random_grid = {'n_estimators': n_estimators,
               'max_features': max_features,
               'max_depth': max_depth,
               'min_samples_split': min_samples_split,
               'min_samples_leaf': min_samples_leaf}


# rf_random = RandomizedSearchCV(estimator = rf, param_distributions = random_grid,
#                                n_iter = 100, cv = 5, verbose=2, random_state=42, n_jobs = 1)

# rf_random.fit(train_x,train_y)


# print(rf_random.best_params_)
# print('score: {}' .format(rf_random.best_score_))


rf = RandomForestRegressor(n_estimators=500, min_samples_split=2, 
                           min_samples_leaf=1, max_features='sqrt', 
                           max_depth=15)
rf.fit(train_x,train_y)
y_pred = rf.predict(test_x)

RF_score = cross_val_score(rf,test_x,test_y,cv=10)
print('Cross Val Score: ', RF_score.mean())

RF_RMSE = np.sqrt(mean_squared_error(test_y, y_pred))
print('Root Mean Squared Error:', RF_RMSE)

Cross Val Score:  0.838707183638175
Root Mean Squared Error: 0.27086556001851975


from sklearn.neighbors import KNeighborsRegressor

accuracy=[]
k=0
temp=0
for n in range(1,50):
    knn=KNeighborsRegressor(n_neighbors=n)
    result= cross_val_score(knn, test_x, test_y, cv=10)
    accuracy.append(result.mean())
    if (result.mean() > temp):
        temp=result.mean()
        k=n
    
    
plt.figure(figsize=(10,8))
axes = plt.axes()
axes.grid()
plt.plot(range(1,50), accuracy, color='blue', linestyle=':',
        marker='o', markerfacecolor='red',markersize=10 )
plt.title('accuracy vs K-value')
plt.xlabel('K-value')
plt.xticks(range(0,50,2))
plt.ylabel('accuracy')

print('Best K-value: {}'.format(k))

Best K-value: 9


knn=KNeighborsRegressor(n_neighbors=3)
knn.fit(train_x,train_y)
y_pred = knn.predict(test_x)

knn_score = cross_val_score(knn,test_x,test_y,cv=10)
print('Cross Val Score: ', knn_score.mean())

knn_RMSE = np.sqrt(mean_squared_error(test_y, y_pred))
print('Root Mean Squared Error:', knn_RMSE)

Cross Val Score:  0.6588580491418964
Root Mean Squared Error: 0.2975340522610127


print('Linear Regression score: {} , RMSE: {}'.format(linear_score.mean(),linear_RMSE))
print('Ridge Regression score: {} , RMSE: {}'.format(ridge_score.mean(),ridge_RMSE))
print('Lasso Regression score: {} , RMSE: {}'.format(lasso_score.mean() ,lasso_RMSE))
print('KNN Regression score: {} , RMSE: {}'.format(knn_score.mean() ,knn_RMSE))
print('SVM Regression score: {} , RMSE: {}'.format(SVM_score.mean() ,SVM_RMSE))
print('Random Forest score: {} , RMSE: {}'.format(RF_score.mean() ,RF_RMSE))

Linear Regression score: 0.8564504681790399 , RMSE: 86.2354018245843
Ridge Regression score: 0.8606347335018991 , RMSE: 0.27740277050635215
Lasso Regression score: 0.8607689533495189 , RMSE: 0.2765120843288113
KNN Regression score: 0.6588580491418964 , RMSE: 0.2975340522610127
SVM Regression score: 0.7691136815622601 , RMSE: 0.30500190639881103
Random Forest score: 0.838707183638175 , RMSE: 0.27086556001851975


test_df = pd.read_csv("C:\\Users\\91741\\Downloads\\test (2).csv")
test_df.head()


def replace_nan(test_df,cat_nan_feat):
    for feature in cat_nan_feat:
        test_df[feature].fillna('Missing',inplace=True)
replace_nan(test_df,cat_nan_feat)


test_df.head()


temp_df

SaleCondition
Abnorml    0.082192
Alloca     0.017123
Normal     0.801370
Partial    0.092466
Name: SalePrice, dtype: float64


for i,feature in enumerate(cat_var):
    test_df[feature]=test_df[feature].map(all_labels[i])


num_nan_features = [feature for feature in test_df.columns if test_df[feature].dtypes!='O' 
                    and test_df[feature].isnull().sum()>0]


for feature in num_nan_features:
    median=test_df[feature].median()
    
    test_df[feature].fillna(median,inplace=True)


for feature in ['YearBuilt', 'YearRemodAdd', 'GarageYrBlt']:
    test_df[feature] = test_df['YrSold']-test_df[feature]


test_df.head()


test_final=test_df[selected_feat]


l=Lasso(alpha=1e-15)
l.fit(train_x,train_y)
h=l.predict(test_final)

h

array([1063.41780238, 1361.16129202, 1379.74766253, ..., 1714.04332051,
        964.19046163, 1220.71493951])


test_df['SalePrice']=h
test_df.head()


submission_data=test_df[['Id','SalePrice']]


submission_data.to_csv('Suuubbmission.csv',index=False)

	Id	MSSubClass	LotFrontage	LotArea	OverallQual	OverallCond	YearBuilt	YearRemodAdd	MasVnrArea	BsmtFinSF1	BsmtFinSF2	BsmtUnfSF	TotalBsmtSF	1stFlrSF	2ndFlrSF	LowQualFinSF	GrLivArea	BsmtFullBath	BsmtHalfBath	FullBath	HalfBath	BedroomAbvGr	KitchenAbvGr	TotRmsAbvGrd	Fireplaces	GarageYrBlt	GarageCars	GarageArea	WoodDeckSF	OpenPorchSF	EnclosedPorch	3SsnPorch	ScreenPorch	PoolArea	MiscVal	MoSold	YrSold	SalePrice
count	1460.000000	1460.000000	1201.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1452.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1379.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000
mean	730.500000	56.897260	70.049958	10516.828082	6.099315	5.575342	1971.267808	1984.865753	103.685262	443.639726	46.549315	567.240411	1057.429452	1162.626712	346.992466	5.844521	1515.463699	0.425342	0.057534	1.565068	0.382877	2.866438	1.046575	6.517808	0.613014	1978.506164	1.767123	472.980137	94.244521	46.660274	21.954110	3.409589	15.060959	2.758904	43.489041	6.321918	2007.815753	180921.195890
std	421.610009	42.300571	24.284752	9981.264932	1.382997	1.112799	30.202904	20.645407	181.066207	456.098091	161.319273	441.866955	438.705324	386.587738	436.528436	48.623081	525.480383	0.518911	0.238753	0.550916	0.502885	0.815778	0.220338	1.625393	0.644666	24.689725	0.747315	213.804841	125.338794	66.256028	61.119149	29.317331	55.757415	40.177307	496.123024	2.703626	1.328095	79442.502883
min	1.000000	20.000000	21.000000	1300.000000	1.000000	1.000000	1872.000000	1950.000000	0.000000	0.000000	0.000000	0.000000	0.000000	334.000000	0.000000	0.000000	334.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	2.000000	0.000000	1900.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000	2006.000000	34900.000000
25%	365.750000	20.000000	59.000000	7553.500000	5.000000	5.000000	1954.000000	1967.000000	0.000000	0.000000	0.000000	223.000000	795.750000	882.000000	0.000000	0.000000	1129.500000	0.000000	0.000000	1.000000	0.000000	2.000000	1.000000	5.000000	0.000000	1961.000000	1.000000	334.500000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	5.000000	2007.000000	129975.000000
50%	730.500000	50.000000	69.000000	9478.500000	6.000000	5.000000	1973.000000	1994.000000	0.000000	383.500000	0.000000	477.500000	991.500000	1087.000000	0.000000	0.000000	1464.000000	0.000000	0.000000	2.000000	0.000000	3.000000	1.000000	6.000000	1.000000	1980.000000	2.000000	480.000000	0.000000	25.000000	0.000000	0.000000	0.000000	0.000000	0.000000	6.000000	2008.000000	163000.000000
75%	1095.250000	70.000000	80.000000	11601.500000	7.000000	6.000000	2000.000000	2004.000000	166.000000	712.250000	0.000000	808.000000	1298.250000	1391.250000	728.000000	0.000000	1776.750000	1.000000	0.000000	2.000000	1.000000	3.000000	1.000000	7.000000	1.000000	2002.000000	2.000000	576.000000	168.000000	68.000000	0.000000	0.000000	0.000000	0.000000	0.000000	8.000000	2009.000000	214000.000000
max	1460.000000	190.000000	313.000000	215245.000000	10.000000	9.000000	2010.000000	2010.000000	1600.000000	5644.000000	1474.000000	2336.000000	6110.000000	4692.000000	2065.000000	572.000000	5642.000000	3.000000	2.000000	3.000000	2.000000	8.000000	3.000000	14.000000	3.000000	2010.000000	4.000000	1418.000000	857.000000	547.000000	552.000000	508.000000	480.000000	738.000000	15500.000000	12.000000	2010.000000	755000.000000

Data Analysis¶

Numerical Features¶

Year Features¶

Outliers¶

Handling Outliers¶

Categorical Variables¶

Relation between Categorical Variables and SalePrice(Dependent Feature)¶

Feature Engineering¶

Feature Engineering For Training Dataset¶

Handling Missing values For Numerical Variables¶

Handling rare categorical features¶

Feature Scaling¶

Feature Engineering For Test Dataset¶

Feature Selection¶

get_support()¶

Linear Regression¶

Ridge Regression¶

Lasso Regression¶

Compare Linear/Ridge/Lasso¶

SVM Regressor¶

Random Forest Regressor¶

K-Nearest-Neighbour Regressor¶

Working on test dataset¶

	Id	MSSubClass	MSZoning	LotFrontage	LotArea	Street	Alley	LotShape	LandContour	Utilities	LotConfig	LandSlope	Neighborhood	Condition1	Condition2	BldgType	HouseStyle	OverallQual	OverallCond	YearBuilt	YearRemodAdd	RoofStyle	RoofMatl	Exterior1st	Exterior2nd	MasVnrType	MasVnrArea	ExterQual	ExterCond	Foundation	BsmtQual	BsmtCond	BsmtExposure	BsmtFinType1	BsmtFinSF1	BsmtFinType2	BsmtUnfSF	TotalBsmtSF	Heating	HeatingQC	CentralAir	Electrical	1stFlrSF	2ndFlrSF	GrLivArea	BsmtFullBath	BsmtHalfBath	FullBath	HalfBath	BedroomAbvGr	KitchenAbvGr	KitchenQual	TotRmsAbvGrd	Functional	Fireplaces	FireplaceQu	GarageType	GarageYrBlt	GarageFinish	GarageCars	GarageArea	GarageQual	GarageCond	PavedDrive	WoodDeckSF	OpenPorchSF	EnclosedPorch	PoolQC	Fence	MiscFeature	MoSold	YrSold	SaleType	SaleCondition	SalePrice
0	1	60	RL	65.0	8450	Pave	NaN	Reg	Lvl	AllPub	Inside	Gtl	CollgCr	Norm	Norm	1Fam	2Story	7	5	2003	2003	Gable	CompShg	VinylSd	VinylSd	BrkFace	196.0	Gd	TA	PConc	Gd	TA	No	GLQ	706	Unf	150	856	GasA	Ex	Y	SBrkr	856	854	1710	1	0	2	1	3	1	Gd	8	Typ	0	NaN	Attchd	2003.0	RFn	2	548	TA	TA	Y	0	61	0	NaN	NaN	NaN	2	2008	WD	Normal	208500
1	2	20	RL	80.0	9600	Pave	NaN	Reg	Lvl	AllPub	FR2	Gtl	Veenker	Feedr	Norm	1Fam	1Story	6	8	1976	1976	Gable	CompShg	MetalSd	MetalSd	None	0.0	TA	TA	CBlock	Gd	TA	Gd	ALQ	978	Unf	284	1262	GasA	Ex	Y	SBrkr	1262	0	1262	0	1	2	0	3	1	TA	6	Typ	1	TA	Attchd	1976.0	RFn	2	460	TA	TA	Y	298	0	0	NaN	NaN	NaN	5	2007	WD	Normal	181500
2	3	60	RL	68.0	11250	Pave	NaN	IR1	Lvl	AllPub	Inside	Gtl	CollgCr	Norm	Norm	1Fam	2Story	7	5	2001	2002	Gable	CompShg	VinylSd	VinylSd	BrkFace	162.0	Gd	TA	PConc	Gd	TA	Mn	GLQ	486	Unf	434	920	GasA	Ex	Y	SBrkr	920	866	1786	1	0	2	1	3	1	Gd	6	Typ	1	TA	Attchd	2001.0	RFn	2	608	TA	TA	Y	0	42	0	NaN	NaN	NaN	9	2008	WD	Normal	223500
3	4	70	RL	60.0	9550	Pave	NaN	IR1	Lvl	AllPub	Corner	Gtl	Crawfor	Norm	Norm	1Fam	2Story	7	5	1915	1970	Gable	CompShg	Wd Sdng	Wd Shng	None	0.0	TA	TA	BrkTil	TA	Gd	No	ALQ	216	Unf	540	756	GasA	Gd	Y	SBrkr	961	756	1717	1	0	1	0	3	1	Gd	7	Typ	1	Gd	Detchd	1998.0	Unf	3	642	TA	TA	Y	0	35	272	NaN	NaN	NaN	2	2006	WD	Abnorml	140000
4	5	60	RL	84.0	14260	Pave	NaN	IR1	Lvl	AllPub	FR2	Gtl	NoRidge	Norm	Norm	1Fam	2Story	8	5	2000	2000	Gable	CompShg	VinylSd	VinylSd	BrkFace	350.0	Gd	TA	PConc	Gd	TA	Av	GLQ	655	Unf	490	1145	GasA	Ex	Y	SBrkr	1145	1053	2198	1	0	2	1	4	1	Gd	9	Typ	1	TA	Attchd	2000.0	RFn	3	836	TA	TA	Y	192	84	0	NaN	NaN	NaN	12	2008	WD	Normal	250000

	Id	MSSubClass	MSZoning	LotFrontage	LotArea	Street	Alley	LotShape	LandContour	Utilities	LotConfig	LandSlope	Neighborhood	Condition1	Condition2	BldgType	HouseStyle	OverallQual	OverallCond	YearBuilt	YearRemodAdd	RoofStyle	RoofMatl	Exterior1st	Exterior2nd	MasVnrType	MasVnrArea	ExterQual	ExterCond	Foundation	BsmtQual	BsmtCond	BsmtExposure	BsmtFinType1	BsmtFinSF1	BsmtFinType2	BsmtUnfSF	TotalBsmtSF	Heating	HeatingQC	CentralAir	Electrical	1stFlrSF	2ndFlrSF	GrLivArea	BsmtFullBath	BsmtHalfBath	FullBath	HalfBath	BedroomAbvGr	KitchenAbvGr	KitchenQual	TotRmsAbvGrd	Functional	Fireplaces	FireplaceQu	GarageType	GarageYrBlt	GarageFinish	GarageCars	GarageArea	GarageQual	GarageCond	PavedDrive	WoodDeckSF	OpenPorchSF	PoolQC	Fence	MiscFeature	MoSold	YrSold	SaleType	SaleCondition	SalePrice
0	1	60	RL	65.0	8450.0	Pave	NaN	Reg	Lvl	AllPub	Inside	Gtl	CollgCr	Norm	Norm	1Fam	2Story	7	5	2003	2003	Gable	CompShg	VinylSd	VinylSd	BrkFace	196.0	Gd	TA	PConc	Gd	TA	No	GLQ	706.0	Unf	150.0	856.0	GasA	Ex	Y	SBrkr	856.0	854.0	1710.0	1	0	2	1	3	1	Gd	8	Typ	0	NaN	Attchd	2003.0	RFn	2	548.0	TA	TA	Y	0.0	61.0	NaN	NaN	NaN	2	2008	WD	Normal	208500
1	2	20	RL	80.0	9600.0	Pave	NaN	Reg	Lvl	AllPub	FR2	Gtl	Veenker	Feedr	Norm	1Fam	1Story	6	8	1976	1976	Gable	CompShg	MetalSd	MetalSd	None	0.0	TA	TA	CBlock	Gd	TA	Gd	ALQ	978.0	Unf	284.0	1262.0	GasA	Ex	Y	SBrkr	1262.0	0.0	1262.0	0	1	2	0	3	1	TA	6	Typ	1	TA	Attchd	1976.0	RFn	2	460.0	TA	TA	Y	298.0	0.0	NaN	NaN	NaN	5	2007	WD	Normal	181500
2	3	60	RL	68.0	11250.0	Pave	NaN	IR1	Lvl	AllPub	Inside	Gtl	CollgCr	Norm	Norm	1Fam	2Story	7	5	2001	2002	Gable	CompShg	VinylSd	VinylSd	BrkFace	162.0	Gd	TA	PConc	Gd	TA	Mn	GLQ	486.0	Unf	434.0	920.0	GasA	Ex	Y	SBrkr	920.0	866.0	1786.0	1	0	2	1	3	1	Gd	6	Typ	1	TA	Attchd	2001.0	RFn	2	608.0	TA	TA	Y	0.0	42.0	NaN	NaN	NaN	9	2008	WD	Normal	223500
3	4	70	RL	60.0	9550.0	Pave	NaN	IR1	Lvl	AllPub	Corner	Gtl	Crawfor	Norm	Norm	1Fam	2Story	7	5	1915	1970	Gable	CompShg	Wd Sdng	Wd Shng	None	0.0	TA	TA	BrkTil	TA	Gd	No	ALQ	216.0	Unf	540.0	756.0	GasA	Gd	Y	SBrkr	961.0	756.0	1717.0	1	0	1	0	3	1	Gd	7	Typ	1	Gd	Detchd	1998.0	Unf	3	642.0	TA	TA	Y	0.0	35.0	NaN	NaN	NaN	2	2006	WD	Abnorml	140000
4	5	60	RL	84.0	14260.0	Pave	NaN	IR1	Lvl	AllPub	FR2	Gtl	NoRidge	Norm	Norm	1Fam	2Story	8	5	2000	2000	Gable	CompShg	VinylSd	VinylSd	BrkFace	350.0	Gd	TA	PConc	Gd	TA	Av	GLQ	655.0	Unf	490.0	1145.0	GasA	Ex	Y	SBrkr	1145.0	1053.0	2198.0	1	0	2	1	4	1	Gd	9	Typ	1	TA	Attchd	2000.0	RFn	3	836.0	TA	TA	Y	192.0	84.0	NaN	NaN	NaN	12	2008	WD	Normal	250000

	Id	MSSubClass	MSZoning	LotFrontage	LotArea	Street	Alley	LotShape	LandContour	Utilities	LotConfig	LandSlope	Neighborhood	Condition1	Condition2	BldgType	HouseStyle	OverallQual	OverallCond	YearBuilt	YearRemodAdd	RoofStyle	RoofMatl	Exterior1st	Exterior2nd	MasVnrType	MasVnrArea	ExterQual	ExterCond	Foundation	BsmtQual	BsmtCond	BsmtExposure	BsmtFinType1	BsmtFinSF1	BsmtFinType2	BsmtUnfSF	TotalBsmtSF	Heating	HeatingQC	CentralAir	Electrical	1stFlrSF	2ndFlrSF	GrLivArea	BsmtFullBath	FullBath	HalfBath	BedroomAbvGr	KitchenAbvGr	KitchenQual	TotRmsAbvGrd	Functional	Fireplaces	FireplaceQu	GarageType	GarageYrBlt	GarageFinish	GarageCars	GarageArea	GarageQual	GarageCond	PavedDrive	WoodDeckSF	OpenPorchSF	PoolQC	Fence	MiscFeature	MoSold	SaleType	SaleCondition	SalePrice	GarageYrBlt_nan
254	255	20	RL	70.0	8400.0	Pave	Missing	Reg	Lvl	AllPub	Inside	Gtl	NAmes	Norm	Norm	1Fam	1Story	5	6	53	53	Gable	CompShg	MetalSd	MetalSd	None	0.0	TA	Gd	CBlock	TA	TA	No	Rec	922.0	Unf	392.0	1314.0	GasA	TA	Y	SBrkr	1314.0	0.0	1314.0	1	1	0	3	1	TA	5	Typ	0	Missing	Attchd	53.0	RFn	1	294.0	TA	TA	Y	250.0	0.0	Missing	Missing	Missing	6	WD	Normal	145000	0
1066	1067	60	RL	59.0	7837.0	Pave	Missing	IR1	Lvl	AllPub	Inside	Gtl	Gilbert	Norm	Norm	1Fam	2Story	6	7	16	15	Gable	CompShg	VinylSd	VinylSd	None	0.0	Gd	TA	PConc	Gd	TA	No	Unf	0.0	Unf	799.0	799.0	GasA	Gd	Y	SBrkr	799.0	772.0	1571.0	0	2	1	3	1	TA	7	Typ	1	TA	Attchd	16.0	RFn	2	380.0	TA	TA	Y	0.0	40.0	Missing	Missing	Missing	5	WD	Normal	178000	0
638	639	30	RL	67.0	8777.0	Pave	Missing	Reg	Lvl	AllPub	Inside	Gtl	Edwards	Feedr	Norm	1Fam	1Story	5	7	98	58	Gable	CompShg	MetalSd	Wd Sdng	None	0.0	TA	TA	CBlock	Fa	TA	No	Unf	0.0	Unf	796.0	796.0	GasA	Gd	Y	FuseA	796.0	0.0	796.0	0	1	0	2	1	TA	4	Typ	0	Missing	Missing	28.0	Missing	0	0.0	Missing	Missing	P	328.0	0.0	Missing	MnPrv	Missing	5	WD	Normal	85000	1
799	800	50	RL	60.0	7200.0	Pave	Missing	Reg	Lvl	AllPub	Corner	Gtl	SWISU	Feedr	Norm	1Fam	1.5Fin	5	7	70	57	Gable	CompShg	Wd Sdng	Wd Sdng	BrkFace	252.0	TA	TA	BrkTil	Gd	TA	No	ALQ	569.0	Unf	162.0	731.0	GasA	Ex	Y	SBrkr	981.0	787.0	1768.0	1	1	1	3	1	Gd	7	Typ	2	TA	Detchd	68.0	Unf	1	240.0	TA	TA	Y	0.0	0.0	Missing	MnPrv	Missing	6	WD	Normal	175000	0
380	381	50	RL	50.0	5000.0	Pave	Pave	Reg	Lvl	AllPub	Inside	Gtl	SWISU	Norm	Norm	1Fam	1.5Fin	5	6	86	60	Gable	CompShg	BrkFace	Wd Sdng	None	0.0	TA	TA	BrkTil	TA	TA	No	LwQ	218.0	Unf	808.0	1026.0	GasA	TA	Y	SBrkr	1026.0	665.0	1691.0	0	2	0	3	1	Gd	6	Typ	1	Gd	Detchd	86.0	Unf	1	308.0	TA	TA	Y	0.0	0.0	Missing	Missing	Missing	5	WD	Normal	127000	0

	Id	SalePrice	MSSubClass	MSZoning	LotFrontage	LotArea	Street	Alley	LotShape	LandContour	Utilities	LotConfig	Neighborhood	Condition1	Condition2	BldgType	HouseStyle	OverallQual	OverallCond	YearBuilt	YearRemodAdd	Exterior1st	Exterior2nd	MasVnrType	MasVnrArea	ExterQual	ExterCond	Foundation	BsmtQual	BsmtCond	BsmtExposure	BsmtFinType1	BsmtFinSF1	BsmtFinType2	BsmtUnfSF	TotalBsmtSF	Heating	HeatingQC	CentralAir	Electrical	1stFlrSF	2ndFlrSF	GrLivArea	BsmtFullBath	FullBath	HalfBath	BedroomAbvGr	KitchenAbvGr	KitchenQual	TotRmsAbvGrd	Functional	Fireplaces	FireplaceQu	GarageType	GarageYrBlt	GarageFinish	GarageCars	GarageArea	GarageQual	GarageCond	PavedDrive	WoodDeckSF	OpenPorchSF	Fence	MiscFeature	MoSold	SaleType	SaleCondition	GarageYrBlt_nan
0	255	7.530705	0.000000	0.75	0.505952	0.427279	1.0	1.0	0.000000	0.333333	1.0	0.25	0.333333	0.4	1.0	0.75	0.500000	0.444444	0.625	0.389706	0.885246	0.4	0.2	0.25	0.000000	0.333333	0.666667	0.50	0.50	0.75	0.25	0.166667	0.827560	0.666667	0.485295	0.632836	1.0	0.50	1.0	1.000000	0.709071	0.000000	0.595706	0.333333	0.333333	0.0	0.375	0.333333	0.333333	0.250000	1.0	0.000000	0.2	0.8	0.495327	0.666667	0.25	0.313349	0.75	1.0	1.0	0.934344	0.000000	0.75	1.0	0.454545	0.666667	0.75	0.0
1	1067	7.605930	0.235294	0.75	0.375000	0.392509	1.0	1.0	0.333333	0.333333	1.0	0.25	0.619048	0.4	1.0	0.75	1.000000	0.555556	0.750	0.117647	0.262295	0.9	0.8	0.25	0.000000	0.666667	1.000000	1.00	0.75	0.75	0.25	0.833333	0.000000	0.666667	0.692499	0.376617	1.0	0.75	1.0	1.000000	0.435877	0.925289	0.687633	0.000000	0.666667	0.5	0.375	0.333333	0.333333	0.416667	1.0	0.333333	0.6	0.8	0.149533	0.666667	0.50	0.405009	0.75	1.0	1.0	0.000000	0.695354	0.75	1.0	0.363636	0.666667	0.75	0.0
2	639	7.328603	0.058824	0.75	0.470238	0.450562	1.0	1.0	0.000000	0.333333	1.0	0.25	0.142857	0.2	1.0	0.75	0.500000	0.444444	0.750	0.720588	0.967213	0.4	0.1	0.25	0.000000	0.333333	1.000000	0.50	0.25	0.75	0.25	0.833333	0.000000	0.666667	0.691209	0.375124	1.0	0.75	1.0	0.666667	0.433882	0.000000	0.356408	0.000000	0.333333	0.0	0.250	0.333333	0.333333	0.166667	1.0	0.000000	0.2	0.0	0.261682	0.000000	0.00	0.000000	0.00	0.0	0.5	0.969084	0.000000	0.50	1.0	0.363636	0.666667	0.75	1.0
3	800	7.599743	0.176471	0.75	0.386905	0.353168	1.0	1.0	0.000000	0.333333	1.0	0.00	0.285714	0.2	1.0	0.75	0.166667	0.444444	0.750	0.514706	0.950820	0.3	0.1	0.50	0.957995	0.333333	1.000000	0.25	0.75	0.75	0.25	0.666667	0.716985	0.666667	0.309083	0.342786	1.0	1.00	1.0	1.000000	0.546306	0.927048	0.750458	0.333333	0.333333	0.5	0.375	0.333333	0.666667	0.416667	1.0	0.666667	0.6	0.4	0.635514	0.333333	0.25	0.255795	0.75	1.0	1.0	0.000000	0.000000	0.50	1.0	0.454545	0.666667	0.75	0.0
4	381	7.481387	0.176471	0.75	0.267857	0.217299	1.0	0.5	0.000000	0.333333	1.0	0.25	0.285714	0.4	1.0	0.75	0.166667	0.444444	0.625	0.632353	1.000000	0.8	0.1	0.25	0.000000	0.333333	1.000000	0.25	0.50	0.75	0.25	0.333333	0.531198	0.666667	0.696352	0.489552	1.0	0.50	1.0	1.000000	0.570860	0.911511	0.726584	0.000000	0.666667	0.0	0.375	0.333333	0.666667	0.333333	1.0	0.333333	0.8	0.4	0.803738	0.333333	0.25	0.328271	0.75	1.0	1.0	0.000000	0.000000	0.75	1.0	0.363636	0.666667	0.75	0.0

	Id	MSSubClass	LotFrontage	LotArea	OverallQual	OverallCond	YearBuilt	YearRemodAdd	MasVnrArea	BsmtFinSF1	BsmtUnfSF	TotalBsmtSF	1stFlrSF	2ndFlrSF	GrLivArea	BsmtFullBath	BsmtHalfBath	FullBath	HalfBath	BedroomAbvGr	KitchenAbvGr	TotRmsAbvGrd	Fireplaces	GarageYrBlt	GarageCars	GarageArea	WoodDeckSF	OpenPorchSF	MoSold	YrSold	SalePrice
892	893	20	70.0	8414.0	6	8	1963	2003	0.0	663.0	396.0	1059.0	1068.0	0.0	1068.0	0	1	1	0	3	1	6	0	1963.0	1	264.0	192.0	0.0	2	2006	154500
1105	1106	60	98.0	12256.0	8	5	1994	1995	362.0	1032.0	431.0	1463.0	1500.0	1122.0	2622.0	1	0	2	1	3	1	9	2	1994.0	2	712.0	186.0	32.0	4	2010	325000
413	414	30	56.0	8960.0	5	6	1927	1950	0.0	0.0	1008.0	1008.0	1028.0	0.0	1028.0	0	0	1	0	2	1	5	1	1927.0	2	360.0	0.0	0.0	3	2010	115000
522	523	50	50.0	5000.0	6	7	1947	1950	0.0	399.0	605.0	1004.0	1004.0	660.0	1664.0	0	0	2	0	3	1	7	2	1950.0	2	420.0	0.0	24.0	10	2006	159000
1036	1037	20	89.0	12898.0	9	5	2007	2008	70.0	1022.0	598.0	1620.0	1620.0	0.0	1620.0	1	0	2	0	2	1	6	1	2008.0	3	912.0	228.0	0.0	9	2009	315500

	Id	SalePrice	MSSubClass	LotFrontage	LotArea	OverallQual	OverallCond	YearBuilt	YearRemodAdd	MasVnrArea	BsmtFinSF1	BsmtUnfSF	TotalBsmtSF	1stFlrSF	2ndFlrSF	GrLivArea	BsmtFullBath	BsmtHalfBath	FullBath	HalfBath	BedroomAbvGr	TotRmsAbvGrd	Fireplaces	GarageYrBlt	GarageCars	GarageArea	WoodDeckSF	OpenPorchSF	MoSold	YrSold
0	893	7.699558	0.000000	0.505952	0.427808	0.500	0.857143	0.338583	0.050000	0.000000	0.757199	0.475890	0.505970	0.575363	0.000000	0.422665	0.0	0.5	0.333333	0.0	0.500000	0.333333	0.000000	0.488636	0.25	0.281375	0.877298	0.000000	0.090909	0.00
1	1106	7.977640	0.235294	0.839286	0.665225	0.750	0.428571	0.125984	0.250000	0.993476	0.859374	0.497296	0.706965	0.789418	0.966352	0.969231	0.5	0.0	0.666667	0.5	0.500000	0.666667	0.666667	0.181818	0.50	0.758860	0.872291	0.715990	0.272727	1.00
2	414	7.584483	0.058824	0.339286	0.461548	0.375	0.571429	0.653543	1.000000	0.000000	0.000000	0.769914	0.480597	0.550242	0.000000	0.401189	0.0	0.0	0.333333	0.0	0.333333	0.222222	0.333333	0.943182	0.50	0.383693	0.000000	0.000000	0.181818	1.00
3	523	7.710604	0.176471	0.267857	0.216839	0.500	0.714286	0.464567	0.933333	0.000000	0.651718	0.592602	0.478607	0.534585	0.926207	0.682363	0.0	0.0	0.666667	0.0	0.500000	0.444444	0.666667	0.636364	0.50	0.447642	0.000000	0.664958	0.818182	0.00
4	1037	7.966859	0.000000	0.732143	0.704897	0.875	0.428571	0.015748	0.016667	0.884755	0.857015	0.589059	0.785075	0.835627	0.000000	0.666121	0.5	0.0	0.666667	0.0	0.333333	0.333333	0.333333	0.011364	0.75	0.972022	0.904361	0.000000	0.727273	0.75

	Id	MSSubClass	MSZoning	LotFrontage	LotArea	Street	Alley	LotShape	LandContour	Utilities	LotConfig	LandSlope	Neighborhood	Condition1	Condition2	BldgType	HouseStyle	OverallQual	OverallCond	YearBuilt	YearRemodAdd	RoofStyle	RoofMatl	Exterior1st	Exterior2nd	MasVnrType	MasVnrArea	ExterQual	ExterCond	Foundation	BsmtQual	BsmtCond	BsmtExposure	BsmtFinType1	BsmtFinSF1	BsmtFinType2	BsmtFinSF2	BsmtUnfSF	TotalBsmtSF	Heating	HeatingQC	CentralAir	Electrical	1stFlrSF	2ndFlrSF	GrLivArea	FullBath	HalfBath	BedroomAbvGr	KitchenAbvGr	KitchenQual	TotRmsAbvGrd	Functional	Fireplaces	FireplaceQu	GarageType	GarageYrBlt	GarageFinish	GarageCars	GarageArea	GarageQual	GarageCond	PavedDrive	WoodDeckSF	OpenPorchSF	ScreenPorch	PoolQC	Fence	MiscFeature	MiscVal	MoSold	YrSold	SaleType	SaleCondition
0	1461	20	RH	80.0	11622	Pave	NaN	Reg	Lvl	AllPub	Inside	Gtl	NAmes	Feedr	Norm	1Fam	1Story	5	6	1961	1961	Gable	CompShg	VinylSd	VinylSd	None	0.0	TA	TA	CBlock	TA	TA	No	Rec	468.0	LwQ	144.0	270.0	882.0	GasA	TA	Y	SBrkr	896	0	896	1	0	2	1	TA	5	Typ	0	NaN	Attchd	1961.0	Unf	1.0	730.0	TA	TA	Y	140	0	120	NaN	MnPrv	NaN	0	6	2010	WD	Normal
1	1462	20	RL	81.0	14267	Pave	NaN	IR1	Lvl	AllPub	Corner	Gtl	NAmes	Norm	Norm	1Fam	1Story	6	6	1958	1958	Hip	CompShg	Wd Sdng	Wd Sdng	BrkFace	108.0	TA	TA	CBlock	TA	TA	No	ALQ	923.0	Unf	0.0	406.0	1329.0	GasA	TA	Y	SBrkr	1329	0	1329	1	1	3	1	Gd	6	Typ	0	NaN	Attchd	1958.0	Unf	1.0	312.0	TA	TA	Y	393	36	0	NaN	NaN	Gar2	12500	6	2010	WD	Normal
2	1463	60	RL	74.0	13830	Pave	NaN	IR1	Lvl	AllPub	Inside	Gtl	Gilbert	Norm	Norm	1Fam	2Story	5	5	1997	1998	Gable	CompShg	VinylSd	VinylSd	None	0.0	TA	TA	PConc	Gd	TA	No	GLQ	791.0	Unf	0.0	137.0	928.0	GasA	Gd	Y	SBrkr	928	701	1629	2	1	3	1	TA	6	Typ	1	TA	Attchd	1997.0	Fin	2.0	482.0	TA	TA	Y	212	34	0	NaN	MnPrv	NaN	0	3	2010	WD	Normal
3	1464	60	RL	78.0	9978	Pave	NaN	IR1	Lvl	AllPub	Inside	Gtl	Gilbert	Norm	Norm	1Fam	2Story	6	6	1998	1998	Gable	CompShg	VinylSd	VinylSd	BrkFace	20.0	TA	TA	PConc	TA	TA	No	GLQ	602.0	Unf	0.0	324.0	926.0	GasA	Ex	Y	SBrkr	926	678	1604	2	1	3	1	Gd	7	Typ	1	Gd	Attchd	1998.0	Fin	2.0	470.0	TA	TA	Y	360	36	0	NaN	NaN	NaN	0	6	2010	WD	Normal
4	1465	120	RL	43.0	5005	Pave	NaN	IR1	HLS	AllPub	Inside	Gtl	StoneBr	Norm	Norm	TwnhsE	1Story	8	5	1992	1992	Gable	CompShg	HdBoard	HdBoard	None	0.0	Gd	TA	PConc	Gd	TA	No	ALQ	263.0	Unf	0.0	1017.0	1280.0	GasA	Ex	Y	SBrkr	1280	0	1280	2	0	2	1	Gd	5	Typ	0	NaN	Attchd	1992.0	RFn	2.0	506.0	TA	TA	Y	0	82	144	NaN	NaN	NaN	0	1	2010	WD	Normal